Fuzzy Support Systems for Discretionary Judicial Decision Making

  • Felipe Lara-Rosano
  • María del Socorro Téllez-Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2774)


Judicial decision making is a very complex decision process because of the variability, flexibility and discretion that characterize it and the numerous factors affecting the results. To aid sentencing decision making, we propose an Intelligent Decision Making Support System based on case based reasoning and fuzzy logic. As an example, in this paper we present a system for abandonment assessment in divorce cases, developed at the Intelligent Systems Laboratory of the National Autonomous University of Mexico.


Fuzzy Logic Case Base Reasoning Legal Reasoning Judicial Decision Multivalued Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Taruffo, M.: Judicial Decisions and Artificial Intelligence. Artificial Intelligence and Law 6, 311–324 (1998)CrossRefGoogle Scholar
  2. 2.
    Ashworth, A.: Sentencing and Criminal Justice. Widenfeld and Nicholson, London (1992a)Google Scholar
  3. 3.
    Council of Europe (CDPC). Consistency in Sentencing R(02), Strasboug, vol. 17 (1993)Google Scholar
  4. 4.
    Hutton, N., Paterson, A., Tata, C., Wilson, J.: A Prototype Sentencing Information System for the High Court of Justiciary: Report of the Study of Feasibility. Scottish Office Home and Health Department, Central Research Unit, Edinburgh (1996)Google Scholar
  5. 5.
    Ashworth, A.: Sentencing Reform Structures. In: Tonry, M. (ed.) Crime and Justice: A Review of Research, vol. 16. University of Chicago Press, Chicago (1992b)Google Scholar
  6. 6.
    Sartor, G.: Logic and Argumentation in Legal Reasoning. Current Legal Theory 25 (1997)Google Scholar
  7. 7.
    Ashley, K.D.: Modeling Legal Argument: Reasoning with Cases and Hypotheticals. Bradford Books/MIT Press, Cambridge, Mass. (1990)Google Scholar
  8. 8.
    Hutton, A., Paterson, A., Tata, C., Wilson, J.: Decision Support for Sentencing in a Common Law Jurisdiction. In: The 5th International Conference on Artificial Intelligence and Law, ACM, New York (1995)Google Scholar
  9. 9.
    Tata, C.: The Application of Judicial Intelligence and ‘Rules’ to Systems Supporting Discretionary Judicial Decision-Making. Artificial Intelligence and Law 6, 203–230 (1998)CrossRefGoogle Scholar
  10. 10.
    Leith, P.: The Judge and the Computer: How Best Decision Support? Artificial Intelligence and Law 6, 289–309 (1998)Google Scholar
  11. 11.
    Xu, M., Hirota, K., Yoshino, H.: A Fuzzy Theoretical Approach to Casebased Representation and Inference in CISG. Artificial Intelligence and Law 7, 259–272 (1999)CrossRefGoogle Scholar
  12. 12.
    Téllez-Silva, M., Lara-Rosano, F., Juárez-Garduño, R.: Fuzzy Logic and Legal Reasoning: An Approach to Legal Inference. In: Lasker, G.E. (ed.) Advances in Systems Research and Cybernetics, vol. III, pp. 91–95. IIAS, Baden-Baden (1999)Google Scholar
  13. 13.
    Shimony, S.E., Nissan, E.: Kappa Calculus and Evidential Strength: a Note on Aqvist’s Logical Theory of Legal Evidence. Artificial Intelligence and Law 9, 153–163 (2001)CrossRefGoogle Scholar
  14. 14.
    Hafner, C.D., Berman, D.H.: The Role of Context in Case-based Legal Reasoning: Teleological, Temporal and Procedural. Artificial Intelligence and Law 10,, 19–64 (2002)CrossRefGoogle Scholar
  15. 15.
    Sartor, G.: Teleological Arguments and Theory-based Dialectics. Artificial Intelligence and Law 10, 95–112 (2002)CrossRefGoogle Scholar
  16. 16.
    Rescher, N.: Many-valued Logic. Mc Graw-Hill, New York (1969)Google Scholar
  17. 17.
    Lara-Rosano, F.: Uncertain Knowledge Representation through Fuzzy Knowledge Networks based on Lukasiewiecz Logic. In: Lasker, G.E. (ed.) Advances in Computer Science, pp. 32–38. IIAS, Windsor (1988)Google Scholar
  18. 18.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems. Academic Press, New York (1980)Google Scholar
  20. 20.
    Leith, P.: Fundamental Flaws in Legal Logic Programming. The Computer Journal 29 (1986a)Google Scholar
  21. 21.
    Leith, P.: Legal Expert Systems: Misunderstanding the Legal Process. Computers and Law 49 (1986b)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Felipe Lara-Rosano
    • 1
  • María del Socorro Téllez-Silva
    • 2
  1. 1.Cybernetics LaboratoryCentre of Applied Sciences and Technological Development 
  2. 2.Graduate DepartmentFaculty of Law, National Autonomous University of Mexico 

Personalised recommendations